A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
JAPAN uses flow-based models to create adaptive prediction areas with better coverage guarantees.
Graph-based weather prediction adapted for local models.
Enhances ocean floor mapping with adaptive uncertainty estimates.
Population attributes are essential in health for understanding who the data represents and precision medicine efforts. Even within disease infection labels, patients can exhibit significant variability; "fever" may mean something different when reported in a doctor's office versus from an online app, precluding direct…
Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested…
Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramatica…
Adapts Altman's model to compositional data for bankruptcy prediction.
VSPS creates flexible prediction regions for multi-target regression with guaranteed coverage.
Stock trend prediction is a challenging task due to the market's noise, and machine learning techniques have recently been successful in coping with this challenge. In this research, we create a novel framework for stock prediction, Dynamic Advisor-Based Ensemble (dynABE). dynABE explores domain-specific areas based on…
KryptoOracle predicts cryptocurrency prices using Twitter sentiments.
Exploration of hydrocarbon resources is a highly complicated and expensive process where various geological, geochemical and geophysical factors are developed then combined together. It is highly significant how to design the seismic data acquisition survey and locate the exploratory wells since incorrect or imprecise …
The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a "library" of candidate prediction models. The SL is not restricted to a single prediction model, but uses …
STMT predicts compounds in unknown areas with trend reflection.
Adaptive filters are applied in several electronic and communication devices like smartphones, advanced headphones, DSP chips, smart antenna, and teleconference systems. Also, they have application in many areas such as system identification, channel equalization, noise reduction, echo cancellation, interference cancel…
Proposes fwelnet to improve prediction using feature information.
Approach for biosensor condition detection using domain adaptation.
PyKale bridges interdisciplinary ML with Python, enabling accurate predictions.
We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence intervals for DTRs has been extensively studied, prediction and tolerance…
Expanding the receptive field to capture large-scale context is key to obtaining good performance in dense prediction tasks, such as human pose estimation. While many state-of-the-art fully-convolutional architectures enlarge the receptive field by reducing resolution using strided convolution or pooling layers, the mo…
New algorithm improves Bayesian neural networks using adaptive importance sampling.
ChatGPT predicts stock trends from Twitter sentiment, showing positive effects.
Predictive policing models can be biased by differential crime reporting rates.
Method estimates uncertainty in spatial predictions by defining an 'area of applicability'.
Predicts clinical events using a landmark approach with machine learning for large biomarker histories.
Enhances linear regression with Kalman filter for loss minimization.
Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually…
The floating body approach to affine surface area is adapted to a holomorphic context providing an alternate approach to Fefferman's invariant hypersurface measure.
As one of standard approaches to train deep neural networks, dropout has been applied to regularize large models to avoid overfitting, and the improvement in performance by dropout has been explained as avoiding co-adaptation between nodes. However, when correlations between nodes are compared after training the networ…
In this paper we consider surfaces which are critical points of the Willmore functional subject to constrained area. In the case of small area we calculate the corrections to the intrinsic geometry induced by the ambient curvature. These estimates together with the choice of an adapted geometric center of mass lead to …
Paper introduces Native Guide for generating time series counterfactual explanations.
Paper uses machine learning for stock prediction using fundamental data.
Adapts AUM to identify ambiguous tasks in crowdsourced learning, improving generalization.
Random forests and LASSO methods improve small area estimation using auxiliary data.
New approach uses autoregressive models to explore and quantify uncertainty in decision-making.
Pyrocast predicts pyrocumulonimbus clouds six hours before they form.
Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: -Data insufficiency:Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results. -Interpretation:The representations lear…
This work studies how an AI-controlled dog-fighting agent with tunable decision-making parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are dev…
A deep learning model is applied for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial relations of traffic flow in large-scale networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to capture …
New methods for predicting compositional data using conformal prediction.
Recent reinforcement learning algorithms, though achieving impressive results in various fields, suffer from brittle training effects such as regression in results and high sensitivity to initialization and parameters. We claim that some of the brittleness stems from variance differences, i.e. when different environmen…
A new DQN algorithm improves portfolio management and risk assessment in digital assets.
Study minimizes Willmore energy with constraints on surface properties.
The use of computational methods to evaluate aesthetics in photography has gained interest in recent years due to the popularization of convolutional neural networks and the availability of new annotated datasets. Most studies in this area have focused on designing models that do not take into account individual prefer…
POLA adapts learning rates for online time series prediction.
New adaptive models improve prediction accuracy with missing data.
Proposes a tabular transformer model to maintain feature effect intelligibility.
JANET improves time series prediction with adaptive uncertainty regions.
Paper proposes a new framework for predictive optimization without training data.